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def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[str] = generate_pascal_triangle(lowerCAmelCase ) for row_idx in range(lowerCAmelCase ): # Print left spaces for _ in range(num_rows - row_idx - 1 ): print(end=" " ) ...
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from collections import defaultdict def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = 1 SCREAMING_SNAKE_CASE_ : Tuple = True for v in tree[start]: if v not in visited: ret += dfs(lowerCAmelCase ) if r...
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from dataclasses import dataclass from typing import List, Optional, Union import numpy as np import PIL from ...utils import BaseOutput, OptionalDependencyNotAvailable, is_torch_available, is_transformers_available from .timesteps import ( fastaa_timesteps, smartaa_timesteps, smartaa_timesteps, smart...
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
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import os from bleurt import score # From: git+https://github.com/google-research/bleurt.git import datasets __lowerCamelCase : str = datasets.logging.get_logger(__name__) __lowerCamelCase : str = '''\ @inproceedings{bleurt, title={BLEURT: Learning Robust Metrics for Text Generation}, a...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_roformer import RoFormerTokenizer from .tokenizati...
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from math import sqrt def _snake_case ( lowerCAmelCase : int ): """simple docstring""" assert isinstance(lowerCAmelCase , lowerCAmelCase ) and ( number >= 0 ), "'number' must been an int and positive" SCREAMING_SNAKE_CASE_ : Optional[int] = True # 0 and 1 are...
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import unittest from transformers import SqueezeBertConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran...
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# Copyright 2022 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
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import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.text import TextDatasetReader from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : Union[str, ...
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from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments from transformers.testing_utils import TestCasePlus, require_torch, slow from transformers.utils import is_datasets_available if is_datasets_available(): import datasets class a__ ( A__ ): ...
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import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDConditi...
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import argparse from pathlib import Path from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Path , lowe...
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from collections import OrderedDict from typing import Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...feature_extraction_utils import FeatureExtractionMixin from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...tokenization_utils_ba...
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def _snake_case ( lowerCAmelCase : int ): """simple docstring""" return 1 if digit in (0, 1) else (digit * factorial(digit - 1 )) def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[int] = 0 SCREAMING...
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from collections import Counter from pathlib import Path from typing import Optional, Tuple import yaml class a__ ( yaml.SafeLoader ): def __UpperCamelCase ( self : str,_A : List[str] ): """simple docstring""" SCREAMING_SNAKE_CASE_...
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from math import pow, sqrt def _snake_case ( *lowerCAmelCase : float ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = len(lowerCAmelCase ) > 0 and all(value > 0.0 for value in values ) return result def _snake_case ( lowerCAmelCase : float , lo...
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from __future__ import annotations from math import pi, sqrt def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : float ): """simple docstring""" if inductance <= 0: raise ValueError("Inductance cannot be 0 or negative" ) elif capacitance <= 0: raise ValueErro...
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import os from distutils.util import strtobool def _snake_case ( lowerCAmelCase : Dict , lowerCAmelCase : Tuple ): """simple docstring""" for e in env_keys: SCREAMING_SNAKE_CASE_ : Dict = int(os.environ.get(lowerCAmelCase , -1 ) ) if val >= 0: retur...
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def _snake_case ( lowerCAmelCase : list ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase ) for i in range(1 , lowerCAmelCase ): SCREAMING_SNAKE_CASE_ : int = collection[i] SCREAMING_SNAKE_CASE_ : Any = 0 SCRE...
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import argparse import os import pickle import sys import torch from transformers import TransfoXLConfig, TransfoXLLMHeadModel, load_tf_weights_in_transfo_xl from transformers.models.transfo_xl import tokenization_transfo_xl as data_utils from transformers.models.transfo_xl.tokenization_transfo_xl import CORPUS_NAME,...
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from collections.abc import Sequence from queue import Queue class a__ : def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ): """simple docstring""" ...
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import argparse import json from typing import List from ltp import LTP from transformers import BertTokenizer def _snake_case ( lowerCAmelCase : Dict ): """simple docstring""" if ( (cp >= 0X4_E_0_0 and cp <= 0X9_F_F_F) or (cp >= 0X3_4_0_0 and cp <= 0X4_D_B_F) # or (c...
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def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ): """simple docstring""" while b: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = b, a % b return a def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : ...
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def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : list[float] ): """simple docstring""" if discount_rate < 0: raise ValueError("Discount rate cannot be negative" ) if not cash_flows: raise ValueError("Cash flows list cannot be empty" ) SCREAMING_SNAKE_CASE_ : ...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) __lowerCamelCase : Dict = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']} try...
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import cva import numpy as np class a__ : def __init__( self : Any,_A : float,_A : int ): """simple docstring""" if k in (0.04, 0.06): SCREAMING_SNAKE_CASE_ : Union[str, Any] = k SCREAMING_SNAKE_CASE_ :...
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import argparse from pathlib import Path from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Path , lowe...
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import json import os import tempfile import unittest import unittest.mock as mock from pathlib import Path from requests.exceptions import HTTPError from transformers.utils import ( CONFIG_NAME, FLAX_WEIGHTS_NAME, TF2_WEIGHTS_NAME, TRANSFORMERS_CACHE, WEIGHTS_NAME, cached_file, get_file_f...
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import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available():...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_sentencepiece_available __lowerCamelCase : Any = {} try: if not is_sentencepiece_available(): raise OptionalDependencyNotAvailable() except OptionalDependencyNotAvailable: pass else: __lowerCam...
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import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging __lowerCamelCase : List[str] = logging.get_logger(__name__) __lowerCamelCase : Tuple ...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available __lowerCamelCase : str = { '''configuration_bridgetower''': [ '''BRIDGETOWER_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BridgeTowerConfig''', '''...
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from __future__ import annotations import os import tempfile import unittest from transformers import ConvBertConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra...
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import absl # noqa: F401 # Here to have a nice missing dependency error message early on import nltk # noqa: F401 # Here to have a nice missing dependency error message early on import numpy # noqa: F401 # Here to have a nice missing dependency error message early on import six # noqa: F401 # Here to have a nice mi...
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def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Tuple = int(lowerCAmelCase ) if decimal in (0, 1): # Exit cases for the recursion return str(lowerCAmelCase ) SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict ...
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from dataclasses import dataclass, field from typing import TYPE_CHECKING, Any, ClassVar, Dict, List, Optional, Union import pyarrow as pa if TYPE_CHECKING: from .features import FeatureType @dataclass class a__ : A = 42 A = None # Automatically constructed ...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available __lowerCamelCase : Union[str, Any] = { '''configuration_chinese_clip''': [ '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ChineseCLIPConfig''...
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import argparse import os import re __lowerCamelCase : int = '''src/diffusers''' # Pattern that looks at the indentation in a line. __lowerCamelCase : Union[str, Any] = re.compile(R'''^(\s*)\S''') # Pattern that matches `"key":" and puts `key` in group 0. __lowerCamelCase : List[Any] ...
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import argparse import re from flax.traverse_util import flatten_dict, unflatten_dict from tax import checkpoints from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model from transformers.util...
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import json import os from dataclasses import dataclass from functools import partial from typing import Callable import flax.linen as nn import jax import jax.numpy as jnp import joblib import optax import wandb from flax import jax_utils, struct, traverse_util from flax.serialization import from_bytes, to_bytes from...
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from math import factorial, radians def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 1_8 , lowerCAmelCase : int = 1_0 ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[str] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0) ...
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from __future__ import annotations def _snake_case ( lowerCAmelCase : list[list[int]] ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[int] = len(lowerCAmelCase ) # We need to create solution object to save path. SCREAMING_SNAKE_CASE_ : str = [[0 ...
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from functools import lru_cache @lru_cache def _snake_case ( lowerCAmelCase : int ): """simple docstring""" if num < 0: raise ValueError("Number should not be negative." ) return 1 if num in (0, 1) else num * factorial(num - 1 ) if __name__ == "__main__": import doctest doct...
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import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging __lowerCamelCase : List[str] = logging.get_logger(__name__) __lowerCamelCase : Tuple ...
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from collections import defaultdict def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = 1 SCREAMING_SNAKE_CASE_ : Tuple = True for v in tree[start]: if v not in visited: ret += dfs(lowerCAmelCase ) if r...
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from math import factorial, pi def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 3_0 ): """simple docstring""" if not isinstance(lowerCAmelCase , (int, float) ): raise ValueError("maclaurin_sin() requires either an int or float for theta" ) if not isinst...
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
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from __future__ import annotations import requests __lowerCamelCase : int = set( '''approved_at_utc approved_by author_flair_background_color author_flair_css_class author_flair_richtext author_flair_template_id author_fullname author_premium can_mod_post category clicked content_categories created_utc...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_roformer import RoFormerTokenizer from .tokenizati...
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from collections import defaultdict from math import gcd def _snake_case ( lowerCAmelCase : int = 1_5_0_0_0_0_0 ): """simple docstring""" SCREAMING_SNAKE_CASE_ : defaultdict = defaultdict(lowerCAmelCase ) SCREAMING_SNAKE_CASE_ : str = 2 while 2 * euclid_m *...
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import unittest from transformers import SqueezeBertConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran...
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def _snake_case ( lowerCAmelCase : int ): """simple docstring""" return sum(i for i in range(1 , number // 2 + 1 ) if number % i == 0 ) == number if __name__ == "__main__": print('''Program to check whether a number is a Perfect number or not...''') __lowerCamelCase : Any = ...
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import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.text import TextDatasetReader from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : Union[str, ...
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def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ): """simple docstring""" while b: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = b, a % b return a def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : ...
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import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDConditi...
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import platform from argparse import ArgumentParser import huggingface_hub from .. import __version__ as version from ..utils import is_accelerate_available, is_torch_available, is_transformers_available, is_xformers_available from . import BaseDiffusersCLICommand def _snake_case ( lowerCAmelCase : ...
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from collections import OrderedDict from typing import Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...feature_extraction_utils import FeatureExtractionMixin from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...tokenization_utils_ba...
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import pickle import shutil import tempfile import unittest from transformers import SPIECE_UNDERLINE, XLMRobertaTokenizer, XLMRobertaTokenizerFast from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow from transformers.utils import cached_property from ...test_tokeniza...
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from collections import Counter from pathlib import Path from typing import Optional, Tuple import yaml class a__ ( yaml.SafeLoader ): def __UpperCamelCase ( self : str,_A : List[str] ): """simple docstring""" SCREAMING_SNAKE_CASE_...
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from typing import Callable, Optional, Union from ...configuration_utils import PretrainedConfig from ...utils import logging __lowerCamelCase : Any = logging.get_logger(__name__) __lowerCamelCase : Tuple = { '''microsoft/xprophetnet-large-wiki100-cased''': ( '''https://huggingface...
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from __future__ import annotations from math import pi, sqrt def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : float ): """simple docstring""" if inductance <= 0: raise ValueError("Inductance cannot be 0 or negative" ) elif capacitance <= 0: raise ValueErro...
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import json import os import sys import tempfile import unittest from pathlib import Path from shutil import copyfile from huggingface_hub import HfFolder, Repository, create_repo, delete_repo from requests.exceptions import HTTPError import transformers from transformers import ( CONFIG_MAPPING, FEATURE_EXTR...
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def _snake_case ( lowerCAmelCase : list ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase ) for i in range(1 , lowerCAmelCase ): SCREAMING_SNAKE_CASE_ : int = collection[i] SCREAMING_SNAKE_CASE_ : Any = 0 SCRE...
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__lowerCamelCase : Tuple = 2_56 # Modulus to hash a string __lowerCamelCase : int = 1_00_00_03 def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : str ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase ) SCREAM...
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from collections.abc import Sequence from queue import Queue class a__ : def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ): """simple docstring""" ...
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import os from tempfile import TemporaryDirectory from unittest import TestCase import pytest from absl.testing import parameterized from datasets import config from datasets.arrow_reader import HF_GCP_BASE_URL from datasets.builder import DatasetBuilder from datasets.dataset_dict import IterableDatasetDict from data...
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def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ): """simple docstring""" while b: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = b, a % b return a def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : ...
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def _snake_case ( lowerCAmelCase : list ): """simple docstring""" if len(lowerCAmelCase ) <= 1: return [tuple(lowerCAmelCase )] SCREAMING_SNAKE_CASE_ : Tuple = [] def generate(lowerCAmelCase : int , lowerCAmelCase : list ): if k == 1: res.ap...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) __lowerCamelCase : Dict = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']} try...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tokenizers_available, is_torch_available __lowerCamelCase : Dict = { '''configuration_biogpt''': ['''BIOGPT_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''BioGptConfig'''], '''tokenization_biogpt''': ['''Bi...
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import argparse from pathlib import Path from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Path , lowe...
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import mpmath # for roots of unity import numpy as np class a__ : def __init__( self : int,_A : Optional[int]=None,_A : Any=None ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Optional[Any] = list(poly_a or [0] )[:...
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import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available():...
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class a__ : def __init__( self : Optional[Any],_A : int,_A : List[str]=None,_A : Tuple=None ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Tuple = data SCREAMING_SNAKE_CASE_ : int = previous ...
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import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging __lowerCamelCase : List[str] = logging.get_logger(__name__) __lowerCamelCase : Tuple ...
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import baseaa def _snake_case ( lowerCAmelCase : str ): """simple docstring""" return baseaa.aaaencode(string.encode("utf-8" ) ) def _snake_case ( lowerCAmelCase : bytes ): """simple docstring""" return baseaa.aaadecode(lowerCAmelCase ).decode("utf-8" ) if...
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from __future__ import annotations import os import tempfile import unittest from transformers import ConvBertConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra...
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from ...configuration_utils import PretrainedConfig from ...utils import logging __lowerCamelCase : Union[str, Any] = logging.get_logger(__name__) __lowerCamelCase : Dict = { '''google/vivit-b-16x2-kinetics400''': ( '''https://huggingface.co/google/vivit-b-16x2-kinetics400/resolve/m...
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def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Tuple = int(lowerCAmelCase ) if decimal in (0, 1): # Exit cases for the recursion return str(lowerCAmelCase ) SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict ...
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import math def _a ( a :int ) -> bool: return math.sqrt(a ) * math.sqrt(a ) == num def _a ( a :int ) -> bool: a = 0 a = n while left <= right: a = (left + right) // 2 if mid**2 == n: return True elif mid**2 > n: ...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available __lowerCamelCase : Union[str, Any] = { '''configuration_chinese_clip''': [ '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ChineseCLIPConfig''...
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'''simple docstring''' import unittest from diffusers.pipelines.pipeline_utils import is_safetensors_compatible class __A ( unittest.TestCase ): def _lowercase (self : Any ): UpperCAmelCase_ = [ "safety_checker/pytorch_model.bin", ...
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import argparse import re from flax.traverse_util import flatten_dict, unflatten_dict from tax import checkpoints from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model from transformers.util...
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'''simple docstring''' from ...configuration_utils import PretrainedConfig from ...utils import logging lowerCamelCase : List[Any] = logging.get_logger(__name__) lowerCamelCase : int = {'ctrl': 'https://huggingface.co/ctrl/resolve/main/config.json'} class __lowerCAmelCase (lo...
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from math import factorial, radians def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 1_8 , lowerCAmelCase : int = 1_0 ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[str] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0) ...
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'''simple docstring''' import requests lowercase : List[str] = 'YOUR API KEY' def lowerCAmelCase_ ( snake_case__ , snake_case__ = giphy_api_key ): '''simple docstring''' A : str = '''+'''.join(query.split() ) A ...
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from functools import lru_cache @lru_cache def _snake_case ( lowerCAmelCase : int ): """simple docstring""" if num < 0: raise ValueError("Number should not be negative." ) return 1 if num in (0, 1) else num * factorial(num - 1 ) if __name__ == "__main__": import doctest doct...
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'''simple docstring''' from __future__ import annotations from typing import Any def a_ ( lowerCamelCase : list ): if not postfix_notation: return 0 lowerCAmelCase = {'+', '-', '*', '/'} lowerCAmelCase = [] for token in postfix_notation: ...
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from collections import defaultdict def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = 1 SCREAMING_SNAKE_CASE_ : Tuple = True for v in tree[start]: if v not in visited: ret += dfs(lowerCAmelCase ) if r...
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from .imports import is_tqdm_available if is_tqdm_available(): from tqdm.auto import tqdm as _tqdm from ..state import PartialState def UpperCAmelCase_ ( __snake_case = True , *__snake_case , **__snake_case ) -> Tuple: """simple docstring""" if not is_tqdm_a...
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
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from typing import Dict from .base import GenericTensor, Pipeline class __A( a ): def SCREAMING_SNAKE_CASE_ ( self , _snake_case=None , _snake_case=None , _snake_case=None , **_snake_case ) -> Optional[Any]: '''simple docstring''' if to...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_roformer import RoFormerTokenizer from .tokenizati...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_tf_available, is_torch_available lowercase_ = { "configuration_transfo_xl": ["TRANSFO_XL_PRETRAINED_CONFIG_ARCHIVE_MAP", "TransfoXLConfig"], "tokenization_transfo_xl": ["TransfoXLCorpus",...
7
import unittest from transformers import SqueezeBertConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran...
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import unittest import numpy as np from transformers.testing_utils import require_pytesseract, require_torch from transformers.utils import is_pytesseract_available, is_torch_available from ...test_image_processing_common import ImageProcessingSavingTestMixin, prepare_image_inputs if is_torch_availab...
8
import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.text import TextDatasetReader from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : Union[str, ...
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from argparse import ArgumentParser, Namespace from ..utils import logging from . import BaseTransformersCLICommand def _UpperCamelCase ( lowercase__ ): return ConvertCommand( args.model_type , args.tf_checkpoint , args.pytorch_dump_output , args.c...
9
import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDConditi...
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import os from shutil import copyfile from typing import Any, Dict, List, Optional, Tuple import sentencepiece as spm from ...tokenization_utils import PreTrainedTokenizer from ...utils import logging __A = logging.get_logger(__name__) __A = "▁" __A = {"vocab_file": "sentenc...
10
from collections import OrderedDict from typing import Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...feature_extraction_utils import FeatureExtractionMixin from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...tokenization_utils_ba...
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import argparse import torch from transformers import ( UniSpeechSatConfig, UniSpeechSatForAudioFrameClassification, UniSpeechSatForSequenceClassification, UniSpeechSatForXVector, WavaVecaFeatureExtractor, logging, ) logging.set_verbosity_info() lowerCAmelCase__ = logging.get_logge...
11
from collections import Counter from pathlib import Path from typing import Optional, Tuple import yaml class a__ ( yaml.SafeLoader ): def __UpperCamelCase ( self : str,_A : List[str] ): """simple docstring""" SCREAMING_SNAKE_CASE_...
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import numpy # List of input, output pairs UpperCAmelCase_ = ( ((5, 2, 3), 15), ((6, 5, 9), 25), ((11, 12, 13), 41), ((1, 1, 1), 8), ((11, 12, 13), 41), ) UpperCAmelCase_ = (((515, 22, 13), 555), ((61, 35, 49), 150)) UpperCAmelCase_ = [2, 4, 1, 5] UpperCAmelCase_ ...
12
from __future__ import annotations from math import pi, sqrt def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : float ): """simple docstring""" if inductance <= 0: raise ValueError("Inductance cannot be 0 or negative" ) elif capacitance <= 0: raise ValueErro...
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lowerCAmelCase : Optional[Any] = range(2, 20 + 1) lowerCAmelCase : Dict = [10**k for k in range(ks[-1] + 1)] lowerCAmelCase : dict[int, dict[int, list[list[int]]]] = {} def A_ ( _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCase , _UpperCAmelCas...
13
def _snake_case ( lowerCAmelCase : list ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase ) for i in range(1 , lowerCAmelCase ): SCREAMING_SNAKE_CASE_ : int = collection[i] SCREAMING_SNAKE_CASE_ : Any = 0 SCRE...
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import inspect import os import torch from transformers import AutoModel from transformers.testing_utils import mockenv_context from transformers.trainer_utils import set_seed import accelerate from accelerate.accelerator import Accelerator from accelerate.state import AcceleratorState from accelerate.test_utils...
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from collections.abc import Sequence from queue import Queue class a__ : def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ): """simple docstring""" ...
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from typing import Dict, Iterable, Optional, Union import numpy as np from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict from ...image_transforms import normalize, rescale, resize, to_channel_dimension_format, to_pil_image from ...image_utils import ( IMAGENET_STANDARD_MEAN, ...
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def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ): """simple docstring""" while b: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = b, a % b return a def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : ...
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"""simple docstring""" def __UpperCAmelCase ( __lowerCamelCase ) -> int: if a < 0: raise ValueError('''Input value must be a positive integer''' ) elif isinstance(__lowerCamelCase , __lowerCamelCase ): raise TypeError('''Input value must be a \'int...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) __lowerCamelCase : Dict = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']} try...
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"""simple docstring""" import time import unittest from transformers import is_torch_available from transformers.testing_utils import require_torch, torch_device from ..test_modeling_common import ids_tensor if is_torch_available(): import torch from transformers.generation import ( MaxLengthCr...
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import argparse from pathlib import Path from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Path , lowe...
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import os import unittest from transformers.models.bartpho.tokenization_bartpho import VOCAB_FILES_NAMES, BartphoTokenizer from transformers.testing_utils import get_tests_dir from ...test_tokenization_common import TokenizerTesterMixin __A =get_tests_dir('''fixtures/test_sentencepiece_bpe.model''') class ...
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import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available():...
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lowercase : Union[str, Any] = """ABCDEFGHIJKLMNOPQRSTUVWXYZ""" def _snake_case( ) -> None: lowercase : Tuple = input("""Enter message: """ ) lowercase : Dict = input("""Enter key [alphanumeric]: """ ) lowercase ...
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import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging __lowerCamelCase : List[str] = logging.get_logger(__name__) __lowerCamelCase : Tuple ...
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import argparse import torch from transformers import BertForMaskedLM if __name__ == "__main__": SCREAMING_SNAKE_CASE : int = argparse.ArgumentParser( description=( "Extraction some layers of the full BertForMaskedLM or RObertaForMaskedLM for Transfer Learned" " Dist...
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from __future__ import annotations import os import tempfile import unittest from transformers import ConvBertConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra...
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'''simple docstring''' def UpperCAmelCase_ ( __lowercase : dict ) -> set: '''simple docstring''' _UpperCAmelCase = set() # edges = list of graph's edges _UpperCAmelCase = get_edges(__lowercase ) # While there are...
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def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Tuple = int(lowerCAmelCase ) if decimal in (0, 1): # Exit cases for the recursion return str(lowerCAmelCase ) SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict ...
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'''simple docstring''' UpperCamelCase__: Tuple = 8.314462 # Unit - J mol-1 K-1 def snake_case_ ( _lowerCAmelCase : float , _lowerCAmelCase : float , _lowerCAmelCase : float ) -> float: if moles < 0 or kelvin < 0 or volume < 0...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available __lowerCamelCase : Union[str, Any] = { '''configuration_chinese_clip''': [ '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ChineseCLIPConfig''...
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import json import os from pathlib import Path import pytest from datasets.download.download_config import DownloadConfig from datasets.download.download_manager import DownloadManager from datasets.utils.file_utils import hash_url_to_filename snake_case_ = 'http://www.mocksite.com/file1...
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import argparse import re from flax.traverse_util import flatten_dict, unflatten_dict from tax import checkpoints from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model from transformers.util...
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"""simple docstring""" from typing import List, Optional from tokenizers import ByteLevelBPETokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_blenderbot_small import BlenderbotSmallTokenizer UpperCAmelCase__ : Optiona...
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from math import factorial, radians def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 1_8 , lowerCAmelCase : int = 1_0 ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[str] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0) ...
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from math import asin, atan, cos, radians, sin, sqrt, tan _snake_case = 6_3_7_8_1_3_7.0 _snake_case = 6_3_5_6_7_5_2.3_1_4_2_4_5 _snake_case = 6378137 def lowerCAmelCase_ ( snake_case_,snake_case_,snake_case_,snake_case_ ): _A : Any ...
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from functools import lru_cache @lru_cache def _snake_case ( lowerCAmelCase : int ): """simple docstring""" if num < 0: raise ValueError("Number should not be negative." ) return 1 if num in (0, 1) else num * factorial(num - 1 ) if __name__ == "__main__": import doctest doct...
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'''simple docstring''' import argparse import json import math import os import time import traceback import zipfile from collections import Counter import requests def lowerCamelCase (_SCREAMING_SNAKE_CASE : Dict , _SCREAMING_SNAKE_CASE : Any=None ): __a : Tu...
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from collections import defaultdict def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = 1 SCREAMING_SNAKE_CASE_ : Tuple = True for v in tree[start]: if v not in visited: ret += dfs(lowerCAmelCase ) if r...
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'''simple docstring''' from __future__ import annotations def __lowerCamelCase ( A__ , A__ ) -> bool: """simple docstring""" if len(A__ ) == 0: return False UpperCamelCase = len(A__ ) // 2 if a_list[midpoint] == item: ...
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
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import inspect import unittest from transformers import DecisionTransformerConfig, is_torch_available from transformers.testing_utils import require_torch, slow, torch_device from ...generation.test_utils import GenerationTesterMixin from ...test_configuration_common import ConfigTester from ......
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_roformer import RoFormerTokenizer from .tokenizati...
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def a ( snake_case__: int , snake_case__: int ): '''simple docstring''' return 1 if input_a == input_a else 0 def a ( ): '''simple docstring''' assert xnor_gate(0 , 0 ) == 1 assert xnor_gate(0 , 1 ) == 0 ...
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import unittest from transformers import SqueezeBertConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran...
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'''simple docstring''' from ...configuration_utils import PretrainedConfig class lowerCamelCase_ (snake_case__ ): '''simple docstring''' __UpperCamelCase: Dict = "bert-generation" def __init__( self : str , A : str=50358 , A : in...
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import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.text import TextDatasetReader from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : Union[str, ...
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import inspect import warnings from typing import Any, Dict, Optional, Union from packaging import version def SCREAMING_SNAKE_CASE_ ( *__A : str , __A : Optional[Union[Dict, Any]] = None , __A : Tuple=True , __A : int=2 ) -> Optional[Any]: """si...
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import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDConditi...
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"""simple docstring""" import operator as op __A : Union[str, Any] = '''scaler.pt''' __A : List[str] = '''pytorch_model''' __A : List[Any] = '''random_states''' __A : int = '''optimizer''' __A : List[Any] = '''scheduler''' _...
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from collections import OrderedDict from typing import Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...feature_extraction_utils import FeatureExtractionMixin from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...tokenization_utils_ba...
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'''simple docstring''' import logging from transformers import PretrainedConfig A =logging.getLogger(__name__) A ={ 'bertabs-finetuned-cnndm': 'https://huggingface.co/remi/bertabs-finetuned-cnndm-extractive-abstractive-summarization/resolve/main/config.json', } class _a ( __a ): ...
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from collections import Counter from pathlib import Path from typing import Optional, Tuple import yaml class a__ ( yaml.SafeLoader ): def __UpperCamelCase ( self : str,_A : List[str] ): """simple docstring""" SCREAMING_SNAKE_CASE_...
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'''simple docstring''' import os import sys import tempfile import torch from .state import AcceleratorState from .utils import PrecisionType, PrepareForLaunch, is_mps_available, patch_environment def __snake_case( _lowerCAmelCase , _lowerCAmelCase=() , _lowerCAmelCase=None , ...
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from __future__ import annotations from math import pi, sqrt def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : float ): """simple docstring""" if inductance <= 0: raise ValueError("Inductance cannot be 0 or negative" ) elif capacitance <= 0: raise ValueErro...
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def A ( _lowerCamelCase ): '''simple docstring''' _lowerCAmelCase : Tuple = abs(_lowerCamelCase ) _lowerCAmelCase : Optional[int] = 0 while n > 0: res += n % 10 n //= 10 return res def A ...
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def _snake_case ( lowerCAmelCase : list ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase ) for i in range(1 , lowerCAmelCase ): SCREAMING_SNAKE_CASE_ : int = collection[i] SCREAMING_SNAKE_CASE_ : Any = 0 SCRE...
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'''simple docstring''' from math import factorial _lowerCAmelCase = {str(digit): factorial(digit) for digit in range(10)} def _SCREAMING_SNAKE_CASE ( UpperCamelCase ): """simple docstring""" if not isinstance(UpperCamelCase , UpperCamelCase ): raise Ty...
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from collections.abc import Sequence from queue import Queue class a__ : def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ): """simple docstring""" ...
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def SCREAMING_SNAKE_CASE_ ( __magic_name__ : str ) -> str: """simple docstring""" UpperCamelCase :int = 0 # if input_string is "aba" than new_input_string become "a|b|a" UpperCamelCase :Dict = """""" UpperCamelCase :int = """""" #...
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def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ): """simple docstring""" while b: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = b, a % b return a def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : ...
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import re from pathlib import Path from unittest import TestCase import pytest @pytest.mark.integration class __lowerCamelCase ( snake_case__): """simple docstring""" def UpperCamelCase ( self , UpperCAmelCase ): """simple...
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from typing import TYPE_CHECKING from ...utils import ( OptionalDependencyNotAvailable, _LazyModule, is_flax_available, is_tf_available, is_torch_available, ) __lowerCamelCase : Dict = {'''configuration_vit_mae''': ['''VIT_MAE_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ViTMAEConfig''']} try...
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"""simple docstring""" def lowercase ( A_ , A_ )-> int: '''simple docstring''' while second != 0: a : List[str] = first & second first ^= second a : Union[str, Any] = c << 1 return first if __...
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import argparse from pathlib import Path from transformers import AutoConfig, AutoTokenizer, RagConfig, RagSequenceForGeneration, RagTokenForGeneration def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : str , lowerCAmelCase : str , lowerCAmelCase : Path , lowe...
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'''simple docstring''' import operator as op _A : Optional[Any] ='''scaler.pt''' _A : Optional[Any] ='''pytorch_model''' _A : int ='''random_states''' _A : List[Any] ='''optimizer''' _A : Dict ='''scheduler''' _A : Dict =...
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import unittest import numpy as np from transformers.testing_utils import require_flax, require_tf, require_torch from transformers.utils import ( expand_dims, flatten_dict, is_flax_available, is_tf_available, is_torch_available, reshape, squeeze, transpose, ) if is_flax_available():...
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'''simple docstring''' from __future__ import annotations from collections.abc import MutableSequence class __UpperCAmelCase : def __init__( self , lowerCAmelCase_ , lowerCAmelCase_ ): """simple docstring""" if len(lowerCAmelCase_ ) != degree +...
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import json import os from functools import lru_cache from typing import List, Optional, Tuple import regex as re from ...tokenization_utils import AddedToken, PreTrainedTokenizer from ...utils import logging __lowerCamelCase : List[str] = logging.get_logger(__name__) __lowerCamelCase : Tuple ...
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__lowercase = [ (1000, '''M'''), (900, '''CM'''), (500, '''D'''), (400, '''CD'''), (100, '''C'''), (90, '''XC'''), (50, '''L'''), (40, '''XL'''), (10, '''X'''), (9, '''IX'''), (5, '''V'''), (4, '''IV'''), (1, '''I'''), ] def lowerCamelCase ( SCREAMING...
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from __future__ import annotations import os import tempfile import unittest from transformers import ConvBertConfig, is_tf_available from transformers.testing_utils import require_tf, slow from ...test_configuration_common import ConfigTester from ...test_modeling_tf_common import TFModelTesterMixin, ids_tensor, ra...
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"""simple docstring""" import math from collections import defaultdict from typing import List, Optional, Tuple, Union import numpy as np import torch from ..configuration_utils import ConfigMixin, register_to_config from .scheduling_utils import KarrasDiffusionSchedulers, SchedulerMixin, SchedulerOutput def...
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def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Tuple = int(lowerCAmelCase ) if decimal in (0, 1): # Exit cases for the recursion return str(lowerCAmelCase ) SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : Dict ...
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"""simple docstring""" from importlib import import_module from .logging import get_logger lowercase_ = get_logger(__name__) class __lowerCAmelCase : '''simple docstring''' def __init__( self , _a , _a=None ): __a = attrs or [] ...
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from typing import TYPE_CHECKING from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available __lowerCamelCase : Union[str, Any] = { '''configuration_chinese_clip''': [ '''CHINESE_CLIP_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''ChineseCLIPConfig''...
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"""simple docstring""" import hashlib import unittest from typing import Dict import numpy as np from transformers import ( MODEL_FOR_MASK_GENERATION_MAPPING, TF_MODEL_FOR_MASK_GENERATION_MAPPING, is_vision_available, pipeline, ) from transformers.pipelines import MaskGenerationPipeline fro...
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import argparse import re from flax.traverse_util import flatten_dict, unflatten_dict from tax import checkpoints from transformers import SwitchTransformersConfig, SwitchTransformersForConditionalGeneration from transformers.modeling_flax_pytorch_utils import load_flax_weights_in_pytorch_model from transformers.util...
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'''simple docstring''' from dataclasses import dataclass, field from typing import Tuple from ..utils import cached_property, is_tf_available, logging, requires_backends from .benchmark_args_utils import BenchmarkArguments if is_tf_available(): import tensorflow as tf lowerCamelCase : Optional[Any] ...
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from math import factorial, radians def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : int = 1_8 , lowerCAmelCase : int = 1_0 ): """simple docstring""" SCREAMING_SNAKE_CASE_ : List[str] = angle_in_degrees - ((angle_in_degrees // 360.0) * 360.0) ...
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import tempfile import unittest import numpy as np from diffusers import ( DDIMScheduler, DPMSolverMultistepScheduler, EulerAncestralDiscreteScheduler, EulerDiscreteScheduler, LMSDiscreteScheduler, OnnxStableDiffusionPipeline, PNDMScheduler, ) from diffusers.utils.t...
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from functools import lru_cache @lru_cache def _snake_case ( lowerCAmelCase : int ): """simple docstring""" if num < 0: raise ValueError("Number should not be negative." ) return 1 if num in (0, 1) else num * factorial(num - 1 ) if __name__ == "__main__": import doctest doct...
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from collections import OrderedDict from typing import Mapping from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto import CONFIG_MAPPING __snake_case :Optional[int] = logging.get_logger(__name__...
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from collections import defaultdict def _snake_case ( lowerCAmelCase : int ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Any = 1 SCREAMING_SNAKE_CASE_ : Tuple = True for v in tree[start]: if v not in visited: ret += dfs(lowerCAmelCase ) if r...
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import os from shutil import copyfile from typing import List, Optional, Tuple from ...tokenization_utils import AddedToken from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import is_sentencepiece_available, logging if is_sentencepiece_available(): from .tokenization_albert impo...
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# Copyright 2021 The HuggingFace Team. All rights reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicabl...
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from collections import Counter import numpy as np from sklearn import datasets from sklearn.model_selection import train_test_split snake_case_ : Optional[Any] = datasets.load_iris() snake_case_ : str = np.array(data["data"]) snake_case_ : Any = np.array(data["tar...
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import json from typing import List, Optional, Tuple from tokenizers import normalizers from tokenizers.pre_tokenizers import BertPreTokenizer, PreTokenizer from ...tokenization_utils_fast import PreTrainedTokenizerFast from ...utils import logging from .tokenization_roformer import RoFormerTokenizer from .tokenizati...
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def A_ ( _lowerCAmelCase , _lowerCAmelCase ) -> List[Any]: # "extended trapezoidal rule" # int(f) = dx/2 * (f1 + 2f2 + ... + fn) UpperCamelCase : Any = (boundary[1] - boundary[0]) / steps UpperCamelCase : List[Any] = boundary[0] UpperCamelCase : List[st...
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import unittest from transformers import SqueezeBertConfig, is_torch_available from transformers.testing_utils import require_sentencepiece, require_tokenizers, require_torch, slow, torch_device from ...test_configuration_common import ConfigTester from ...test_modeling_common import ModelTesterMixin, ids_tensor, ran...
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'''simple docstring''' from typing import Optional, Union import torch from torch import nn from ...configuration_utils import ConfigMixin, register_to_config from ...models.modeling_utils import ModelMixin class snake_case ( __lowerCamelCase , __lowerCamelCase ): """simple docst...
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import pytest from datasets import Dataset, DatasetDict, Features, NamedSplit, Value from datasets.io.text import TextDatasetReader from ..utils import assert_arrow_memory_doesnt_increase, assert_arrow_memory_increases def _snake_case ( lowerCAmelCase : str , lowerCAmelCase : Union[str, ...
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"""simple docstring""" import argparse import os from transformers.utils import direct_transformers_import # All paths are set with the intent you should run this script from the root of the repo with the command # python utils/check_task_guides.py a__ : Dict = '''src/transformers''' a__ ...
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import gc import random import unittest import numpy as np import torch from transformers import CLIPTextConfig, CLIPTextModel, CLIPTokenizer import diffusers from diffusers import ( AutoencoderKL, EulerDiscreteScheduler, StableDiffusionLatentUpscalePipeline, StableDiffusionPipeline, UNetaDConditi...
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'''simple docstring''' import os import sys import unittest a_ : Optional[Any] = os.path.abspath(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))) sys.path.append(os.path.join(git_repo_path, """utils""")) import get_test_info # noqa: E402 from get_test_info import ( # noqa: E4...
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from collections import OrderedDict from typing import Any, Mapping, Optional, Union from ...configuration_utils import PretrainedConfig from ...feature_extraction_utils import FeatureExtractionMixin from ...onnx import OnnxConfig from ...onnx.utils import compute_effective_axis_dimension from ...tokenization_utils_ba...
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'''simple docstring''' import tempfile import torch from diffusers import PNDMScheduler from .test_schedulers import SchedulerCommonTest class a ( _lowerCamelCase ): snake_case_ = (PNDMScheduler,) snake_case_ = (("num_inference_steps", 50),) def A_ ( self : ...
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from collections import Counter from pathlib import Path from typing import Optional, Tuple import yaml class a__ ( yaml.SafeLoader ): def __UpperCamelCase ( self : str,_A : List[str] ): """simple docstring""" SCREAMING_SNAKE_CASE_...
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"""simple docstring""" import inspect import logging import os import random import shutil import tempfile import unittest import pytest import torch from torch import nn from torch.utils.data import DataLoader, TensorDataset from accelerate import Accelerator from accelerate.test_utils import execute_subprocess...
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from __future__ import annotations from math import pi, sqrt def _snake_case ( lowerCAmelCase : float , lowerCAmelCase : float ): """simple docstring""" if inductance <= 0: raise ValueError("Inductance cannot be 0 or negative" ) elif capacitance <= 0: raise ValueErro...
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'''simple docstring''' from .imports import is_rich_available if is_rich_available(): from rich.traceback import install install(show_locals=False) else: raise ModuleNotFoundError("""To use the rich extension, install rich with `pip install rich`""")
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def _snake_case ( lowerCAmelCase : list ): """simple docstring""" SCREAMING_SNAKE_CASE_ : Dict = len(lowerCAmelCase ) for i in range(1 , lowerCAmelCase ): SCREAMING_SNAKE_CASE_ : int = collection[i] SCREAMING_SNAKE_CASE_ : Any = 0 SCRE...
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import copy from typing import TYPE_CHECKING, Any, Mapping, Optional, OrderedDict from packaging import version from ...configuration_utils import PretrainedConfig from ...onnx import OnnxConfig from ...utils import logging from ..auto.configuration_auto import AutoConfig if TYPE_CHECKING: from ......
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from collections.abc import Sequence from queue import Queue class a__ : def __init__( self : int,_A : List[Any],_A : Optional[Any],_A : Optional[int],_A : int=None,_A : List[str]=None ): """simple docstring""" ...
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"""simple docstring""" from typing import List, Optional, Tuple, Union import torch from ...schedulers import DDIMScheduler from ...utils import randn_tensor from ..pipeline_utils import DiffusionPipeline, ImagePipelineOutput class snake_case_( a__ ): def __init__( self : str ...
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def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : int ): """simple docstring""" while b: SCREAMING_SNAKE_CASE_ , SCREAMING_SNAKE_CASE_ : int = b, a % b return a def _snake_case ( lowerCAmelCase : int , lowerCAmelCase : ...
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